Specific aims: The proposed study investigates the impact of hospital and patient characteristics on inpatient charges, length of stay, and mortality for children with asthma while controlling for the severity of asthma. Specifically, in the US between 1988 and 1994, what were the relationship of hospital characteristics (hospital ownership, bed size, location, teaching status, and region), and patient characteristics (patient age, gender, race, income, severity of illness, and payer source) with indices of hospital care (length of stay, total inpatient charges, and in-hospital discharge and mortality rates? Several specific hypotheses will be tested. Objectives: this study will increase our understanding of the impact of hospital and payer groups on children with asthma, especially those with higher severity illness or in poverty. The results should influence payers, providers, policymakers, and advocates to improve access to ambulatory services for children and to recognize and reward hospitals that address the needs of the most vulnerable children with chronic illnesses. Research design and methods: This study involves a cross- sectional analysis of abstracted data from the AHCPR -sponsored Healthcare Cost and Utilization Project Nationwide Impatient Sample, 1 20% stratified probability sample of US community hospitals between 1988 and 1994. All hospital discharges for patients less than 18 years with the principal diagnosis asthma will be included. The severity of illness index will be based upon the All Patient Refined Diagnosis Related Groups. Race/ethnic group and income will be based upon the zip code of residence. The Survey Data Analysis Software System will be used to estimate variances. Multiple regression analysis will be used to measure and test the strength of association between length of stay or total charges and various hospital and patient characteristics. Logistic regression analysis will be used to measure the strength of association between inpatient mortality and various hospital and patient characteristics. Linear regression analyses will be used to model trends in differences of proportions over time.